



import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module

import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask


a      :  Tuple			     =			logging.getLogger(__name__)







class 				_a				(   _lowerCAmelCase					):
			A      		=	'''token-classification'''

			def __init__(self,   SCREAMING_SNAKE_CASE_					)       ->   List[str]:
						if type(SCREAMING_SNAKE_CASE_					) == dict:
									UpperCAmelCase_:		Optional[Any]							      =     Namespace(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Any							      =     import_module("""tasks"""					)
						try:
									UpperCAmelCase_:		Any							      =     getattr(SCREAMING_SNAKE_CASE_,   hparams.task_type					)
									UpperCAmelCase_:		TokenClassificationTask							      =     token_classification_task_clazz()
						except AttributeError:
									raise ValueError(
									    f'Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
									    f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'					)
						UpperCAmelCase_:		Union[str, Any]							      =     self.token_classification_task.get_labels(hparams.labels					)
						UpperCAmelCase_:		Optional[int]							      =     CrossEntropyLoss().ignore_index
						super().__init__(SCREAMING_SNAKE_CASE_,   len(self.labels					),   self.mode					)

			def      __snake_case       (self,   **SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						return self.model(**SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Optional[Any]:
						UpperCAmelCase_:		int							      =     {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
						if self.config.model_type != "distilbert":
									UpperCAmelCase_:		Optional[Any]							      =     (
									    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
									)  # XLM and RoBERTa don"t use token_type_ids

						UpperCAmelCase_:		int							      =     self(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     outputs[0]
						# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
						return {"loss": loss}

			def      __snake_case       (self					)       ->   Dict:
						UpperCAmelCase_:		Optional[int]							      =     self.hparams
						for mode in ["train", "dev", "test"]:
									UpperCAmelCase_:		Tuple							      =     self._feature_file(SCREAMING_SNAKE_CASE_					)
									if os.path.exists(SCREAMING_SNAKE_CASE_					) and not args.overwrite_cache:
												logger.info("""Loading features from cached file %s""",   SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Any							      =     torch.load(SCREAMING_SNAKE_CASE_					)
									else:
												logger.info("""Creating features from dataset file at %s""",   args.data_dir					)
												UpperCAmelCase_:		str							      =     self.token_classification_task.read_examples_from_file(args.data_dir,   SCREAMING_SNAKE_CASE_					)
												UpperCAmelCase_:		Optional[int]							      =     self.token_classification_task.convert_examples_to_features(
												    SCREAMING_SNAKE_CASE_,   self.labels,   args.max_seq_length,   self.tokenizer,   cls_token_at_end=bool(self.config.model_type in ["""xlnet"""]					),   cls_token=self.tokenizer.cls_token,   cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0,   sep_token=self.tokenizer.sep_token,   sep_token_extra=SCREAMING_SNAKE_CASE_,   pad_on_left=bool(self.config.model_type in ["""xlnet"""]					),   pad_token=self.tokenizer.pad_token_id,   pad_token_segment_id=self.tokenizer.pad_token_type_id,   pad_token_label_id=self.pad_token_label_id,   )
												logger.info("""Saving features into cached file %s""",   SCREAMING_SNAKE_CASE_					)
												torch.save(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = False					)       ->   DataLoader:
						UpperCAmelCase_:		List[Any]							      =     self._feature_file(SCREAMING_SNAKE_CASE_					)
						logger.info("""Loading features from cached file %s""",   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     torch.load(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[Any]							      =     torch.tensor([f.input_ids for f in features],   dtype=torch.long					)
						UpperCAmelCase_:		Union[str, Any]							      =     torch.tensor([f.attention_mask for f in features],   dtype=torch.long					)
						if features[0].token_type_ids is not None:
									UpperCAmelCase_:		List[Any]							      =     torch.tensor([f.token_type_ids for f in features],   dtype=torch.long					)
						else:
									UpperCAmelCase_:		List[str]							      =     torch.tensor([0 for f in features],   dtype=torch.long					)
									# HACK(we will not use this anymore soon)
						UpperCAmelCase_:		Union[str, Any]							      =     torch.tensor([f.label_ids for f in features],   dtype=torch.long					)
						return DataLoader(
						    TensorDataset(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					),   batch_size=SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   int:
						"""Compute validation""" ""
						UpperCAmelCase_:		Any							      =     {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
						if self.config.model_type != "distilbert":
									UpperCAmelCase_:		Tuple							      =     (
									    batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
									)  # XLM and RoBERTa don"t use token_type_ids
						UpperCAmelCase_:		int							      =     self(**SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     outputs[:2]
						UpperCAmelCase_:		Any							      =     logits.detach().cpu().numpy()
						UpperCAmelCase_:		int							      =     inputs["""labels"""].detach().cpu().numpy()
						return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Union[str, Any]:
						UpperCAmelCase_:		Any							      =     torch.stack([x["""val_loss"""] for x in outputs]					).mean()
						UpperCAmelCase_:		Union[str, Any]							      =     np.concatenate([x["""pred"""] for x in outputs],   axis=0					)
						UpperCAmelCase_:		Any							      =     np.argmax(SCREAMING_SNAKE_CASE_,   axis=2					)
						UpperCAmelCase_:		Optional[Any]							      =     np.concatenate([x["""target"""] for x in outputs],   axis=0					)

						UpperCAmelCase_:		Optional[Any]							      =     dict(enumerate(self.labels					)					)
						UpperCAmelCase_:		Union[str, Any]							      =     [[] for _ in range(out_label_ids.shape[0]					)]
						UpperCAmelCase_:		List[Any]							      =     [[] for _ in range(out_label_ids.shape[0]					)]

						for i in range(out_label_ids.shape[0]					):
									for j in range(out_label_ids.shape[1]					):
												if out_label_ids[i, j] != self.pad_token_label_id:
															out_label_list[i].append(label_map[out_label_ids[i][j]]					)
															preds_list[i].append(label_map[preds[i][j]]					)

						UpperCAmelCase_:		Optional[int]							      =     {
						    """val_loss""": val_loss_mean,
						    """accuracy_score""": accuracy_score(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					),
						    """precision""": precision_score(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					),
						    """recall""": recall_score(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					),
						    """f1""": fa_score(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					),
						}

						UpperCAmelCase_:		Optional[int]							      =     dict(results.items()					)
						UpperCAmelCase_:		str							      =     results
						return ret, preds_list, out_label_list

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						# when stable
						UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     self._eval_end(SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[int]							      =     ret["""log"""]
						return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Union[str, Any]:
						# updating to test_epoch_end instead of deprecated test_end
						UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[Any]							      =     self._eval_end(SCREAMING_SNAKE_CASE_					)

						# Converting to the dict required by pl
						# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
						# pytorch_lightning/trainer/logging.py#L139
						UpperCAmelCase_:		Optional[Any]							      =     ret["""log"""]
						# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
						return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

			@staticmethod
			def      __snake_case       (SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   Union[str, Any]:
						# Add NER specific options
						BaseTransformer.add_model_specific_args(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
						parser.add_argument(
						    """--task_type""",   default="""NER""",   type=SCREAMING_SNAKE_CASE_,   help="""Task type to fine tune in training (e.g. NER, POS, etc)"""					)
						parser.add_argument(
						    """--max_seq_length""",   default=128,   type=SCREAMING_SNAKE_CASE_,   help=(
						        """The maximum total input sequence length after tokenization. Sequences longer """
						        """than this will be truncated, sequences shorter will be padded."""
						    ),   )

						parser.add_argument(
						    """--labels""",   default="""""",   type=SCREAMING_SNAKE_CASE_,   help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.""",   )
						parser.add_argument(
						    """--gpus""",   default=0,   type=SCREAMING_SNAKE_CASE_,   help="""The number of GPUs allocated for this, it is by default 0 meaning none""",   )

						parser.add_argument(
						    """--overwrite_cache""",   action="""store_true""",   help="""Overwrite the cached training and evaluation sets"""					)

						return parser


if __name__ == "__main__":
	a      :  Optional[Any]			     =			argparse.ArgumentParser()
	add_generic_args(parser, os.getcwd())
	a      :  List[Any]			     =			NERTransformer.add_model_specific_args(parser, os.getcwd())
	a      :  List[str]			     =			parser.parse_args()
	a      :  Tuple			     =			NERTransformer(args)
	a      :  Any			     =			generic_train(model, args)

	if args.do_predict:
		# See https://github.com/huggingface/transformers/issues/3159
		# pl use this default format to create a checkpoint:
		# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
		# /pytorch_lightning/callbacks/model_checkpoint.py#L322
		a      :  Optional[int]			     =			sorted(glob.glob(os.path.join(args.output_dir, 'checkpoint-epoch=*.ckpt'), recursive=True))
		a      :  str			     =			model.load_from_checkpoint(checkpoints[-1])
		trainer.test(model)











import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


a      :  Optional[Any]			     =			logging.getLogger(__name__)







@dataclass(frozen=_lowerCAmelCase					)
class 				_a				:
			A      		=	42
			A      		=	42
			A      		=	None
			A      		=	None
			A      		=	None







@dataclass(frozen=_lowerCAmelCase					)
class 				_a				:
			A      		=	42
			A      		=	None
			A      		=	None
			A      		=	None
			A      		=	None


if is_torch_available():
	import torch
	from torch.utils.data import Dataset







	class 				_a				(   _lowerCAmelCase					):
				A      		=	42

				def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_ = False,   )       ->   Union[str, Any]:
							UpperCAmelCase_:		Optional[Any]							      =     hans_processors[task]()

							UpperCAmelCase_:		Dict							      =     os.path.join(
							    SCREAMING_SNAKE_CASE_,   """cached_{}_{}_{}_{}""".format(
							        """dev""" if evaluate else """train""",   tokenizer.__class__.__name__,   str(SCREAMING_SNAKE_CASE_					),   SCREAMING_SNAKE_CASE_,   ),   )
							UpperCAmelCase_:		Dict							      =     processor.get_labels()
							if tokenizer.__class__ in (
							    RobertaTokenizer,
							    RobertaTokenizerFast,
							    XLMRobertaTokenizer,
							    BartTokenizer,
							    BartTokenizerFast,
							):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										UpperCAmelCase_      ,							UpperCAmelCase_:		Tuple							      =     label_list[2], label_list[1]
							UpperCAmelCase_:		str							      =     label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							UpperCAmelCase_:		int							      =     cached_features_file + """.lock"""
							with FileLock(SCREAMING_SNAKE_CASE_					):
										if os.path.exists(SCREAMING_SNAKE_CASE_					) and not overwrite_cache:
													logger.info(f'Loading features from cached file {cached_features_file}'					)
													UpperCAmelCase_:		Dict							      =     torch.load(SCREAMING_SNAKE_CASE_					)
										else:
													logger.info(f'Creating features from dataset file at {data_dir}'					)

													UpperCAmelCase_:		Union[str, Any]							      =     (
													    processor.get_dev_examples(SCREAMING_SNAKE_CASE_					) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_					)
													)

													logger.info("""Training examples: %s""",   len(SCREAMING_SNAKE_CASE_					)					)
													UpperCAmelCase_:		List[Any]							      =     hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)
													logger.info("""Saving features into cached file %s""",   SCREAMING_SNAKE_CASE_					)
													torch.save(self.features,   SCREAMING_SNAKE_CASE_					)

				def __len__(self					)       ->   Optional[int]:
							return len(self.features					)

				def __getitem__(self,   SCREAMING_SNAKE_CASE_					)       ->   InputFeatures:
							return self.features[i]

				def      __snake_case       (self					)       ->   List[str]:
							return self.label_list


if is_tf_available():
	import tensorflow as tf







	class 				_a				:
				A      		=	42

				def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_ = 128,   SCREAMING_SNAKE_CASE_=False,   SCREAMING_SNAKE_CASE_ = False,   )       ->   Optional[int]:
							UpperCAmelCase_:		Union[str, Any]							      =     hans_processors[task]()
							UpperCAmelCase_:		List[Any]							      =     processor.get_labels()
							if tokenizer.__class__ in (
							    RobertaTokenizer,
							    RobertaTokenizerFast,
							    XLMRobertaTokenizer,
							    BartTokenizer,
							    BartTokenizerFast,
							):
										# HACK(label indices are swapped in RoBERTa pretrained model)
										UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     label_list[2], label_list[1]
							UpperCAmelCase_:		Dict							      =     label_list

							UpperCAmelCase_:		List[str]							      =     processor.get_dev_examples(SCREAMING_SNAKE_CASE_					) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE_					)
							UpperCAmelCase_:		Any							      =     hans_convert_examples_to_features(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

							def gen():
										for ex_index, ex in tqdm.tqdm(enumerate(self.features					),   desc="""convert examples to features"""					):
													if ex_index % 10000 == 0:
																logger.info("""Writing example %d of %d""" % (ex_index, len(SCREAMING_SNAKE_CASE_					))					)

													yield (
													    {
													        "example_id": 0,
													        "input_ids": ex.input_ids,
													        "attention_mask": ex.attention_mask,
													        "token_type_ids": ex.token_type_ids,
													    },
													    ex.label,
													)

							UpperCAmelCase_:		List[str]							      =     tf.data.Dataset.from_generator(
							    SCREAMING_SNAKE_CASE_,   (
							        {
							            """example_id""": tf.intaa,
							            """input_ids""": tf.intaa,
							            """attention_mask""": tf.intaa,
							            """token_type_ids""": tf.intaa,
							        },
							        tf.intaa,
							    ),   (
							        {
							            """example_id""": tf.TensorShape([]					),
							            """input_ids""": tf.TensorShape([None, None]					),
							            """attention_mask""": tf.TensorShape([None, None]					),
							            """token_type_ids""": tf.TensorShape([None, None]					),
							        },
							        tf.TensorShape([]					),
							    ),   )

				def      __snake_case       (self					)       ->   Optional[Any]:
							return self.dataset

				def __len__(self					)       ->   Optional[int]:
							return len(self.features					)

				def __getitem__(self,   SCREAMING_SNAKE_CASE_					)       ->   InputFeatures:
							return self.features[i]

				def      __snake_case       (self					)       ->   Union[str, Any]:
							return self.label_list







class 				_a				(   _lowerCAmelCase					):

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_,   """heuristics_train_set.txt"""					)					),   """train"""					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_					)       ->   Dict:
						return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE_,   """heuristics_evaluation_set.txt"""					)					),   """dev"""					)

			def      __snake_case       (self					)       ->   Optional[int]:
						return ["contradiction", "entailment", "neutral"]

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     []
						for i, line in enumerate(SCREAMING_SNAKE_CASE_					):
									if i == 0:
												continue
									UpperCAmelCase_:		Tuple							      =     """%s-%s""" % (set_type, line[0])
									UpperCAmelCase_:		int							      =     line[5]
									UpperCAmelCase_:		int							      =     line[6]
									UpperCAmelCase_:		List[str]							      =     line[7][2:] if line[7].startswith("""ex"""					) else line[7]
									UpperCAmelCase_:		Any							      =     line[0]
									examples.append(InputExample(guid=SCREAMING_SNAKE_CASE_,   text_a=SCREAMING_SNAKE_CASE_,   text_b=SCREAMING_SNAKE_CASE_,   label=SCREAMING_SNAKE_CASE_,   pairID=SCREAMING_SNAKE_CASE_					)					)
						return examples







def  lowerCAmelCase_	(lowerCAmelCase__:		List[InputExample]		,						lowerCAmelCase__:		List[str]		,						lowerCAmelCase__:		int		,						lowerCAmelCase__:		PreTrainedTokenizer		,						):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     {label: i for i, label in enumerate(lowerCAmelCase__  )}

			UpperCAmelCase_:		Optional[int]							      =     []
			for ex_index, example in tqdm.tqdm(enumerate(lowerCAmelCase__  )		,						desc="""convert examples to features"""  ):
						if ex_index % 1_0_0_0_0 == 0:
									logger.info("""Writing example %d""" % (ex_index)  )

						UpperCAmelCase_:		Tuple							      =     tokenizer(
						    example.text_a		,						example.text_b		,						add_special_tokens=lowerCAmelCase__		,						max_length=lowerCAmelCase__		,						padding="""max_length"""		,						truncation=lowerCAmelCase__		,						return_overflowing_tokens=lowerCAmelCase__		,						)

						UpperCAmelCase_:		Optional[Any]							      =     label_map[example.label] if example.label in label_map else 0

						UpperCAmelCase_:		Tuple							      =     int(example.pairID  )

						features.append(InputFeatures(**lowerCAmelCase__		,						label=lowerCAmelCase__		,						pairID=lowerCAmelCase__  )  )

			for i, example in enumerate(examples[:5]  ):
						logger.info("""*** Example ***"""  )
						logger.info(F'guid: {example}'  )
						logger.info(F'features: {features[i]}'  )

			return features


a      :  Optional[int]			     =			{
    'hans': 3,
}

a      :  Dict			     =			{
    'hans': HansProcessor,
}






